Abstract:

The course will focus on several main topics: defining a basic process in machine learning; Knowing different families of machine learning paradigms, such as regression, classifier and more; Knowledge of different machine learning algorithms such as logistic regression, K-means, and DNNs.

Theme sessions:

1 Introduction: About machine learning, what types of learning exist (classification according to different types of learning), what problems can be solved.

Review: basic concepts in probability, linear algebra and optimization (finding extreme points, Lagrange multipliers, etc.).

2-4 linear regression

Logistic regression.

Regularization (1L and 2-L as an example)

Different price f?unctions (MMSE, cross-entropy)

(precision, recall) evaluation model and measures (CV, K-fold CV) methods

Practice working with the sklearn package

5 Linear SVM classifier and with kernel f?unctions

Implementation practice using sklearn

6 Non-parametric training: decision trees, kNN; Forest Random

(k-means) soft cluster + PCA, LDA, TSNE: download dimension 7

8-10 Basics of DNN

Feed-Forward network

Various activation f?unctions (linear, sigmoid, hyperbolic tangent, SoftMax, ReLu ;)

Back Propagation training

Regularization, and Out-Drop.

Model development practice using KERAS

11-12

(Optional* - may be replaced with other topics at the lecturer's discretion) Advanced architectures in machine learning

Introduction and uses of convolutional networks -CNN

Introduction to sequential models in deep learning: GRU, RNN, LSTM

13 Presentation of work 1 - review of articles

14 Presentation of work 2 - review of final project results